Predicting Violence Against Women Using the Women’s Economic Opportunity Index (WEOI)

An exploration of the the main indicators of the WEOI and what they can tell us about violence against women

Introduction

Even though “achieving gender equality and empowering all women and girls” is among one of the 17 Sustainable Development Goals by the United Nations (“Goal 5 Department of Economic and Social Affairs” n.d.), such goal swims against an enormous wave of violence against women in the contemporary world. Investigation on the causes of such violence is neither exhaustive nor simple. This paper aims to investigate what are possible reasons for the bugging persistence of violence against women, specifically in what regard to a positive attitudes towards violence against women, i.e., more women finding justifiable that a husband would beat their wives.

Believing in the importance of promoting Women’s economic well-being in order to achieve equality, we aim to understand the relationship between violence against women and the Women’s Economic Opportunity Index (WEOI). Providing a violence-free environment for women and girls will immensely support their empowerment, which in turn represents countless gains for society as a whole (King and Mason 2001).

After a brief literature review on the subject of women’s economic well-being, we explain our two-part methodology. We pulled our data from the Organisation for Economic Co-operation and Development (OECD), the United Nations (UN), the World Bank (WB), and the Economist Intelligence Unit (EIU). Our analysis reveals that even though women’s legal status seems to be one of the best predictors within the WEOI for attitude towards violence against women, we must be attentive to the ways in which indicators interact within and outside of the Women’s Economic Opportunity Index affect each other.

Literature Review

Women are disadvantaged in entrepreneurship. Part of that is because structures that push for entrepreneurship have for long pretended to be “genderless” when they are not (Pathak, Goltz, and W. Buche 2013). It is well-known that organizations, by not identifying or addressing the barriers that women might uniquely face, such as child-rearing and domestic labour (Acker 1990), have failed to provide equality in the workplace. Our research dialogues with previous literature in asserting that difficulty in women’s access to finance and labor participation further limit the possibility of women gaining independence from, at best, their domestic and child-rearing duties, and at worse, from their partners. By constraining women from participating in labor in equal and fair manners, as well accessing finance, we argue that this could be increasing violence against women.

Women’s education and legal status can both potentially decrease levels of violence against women. When education is intentionally designed to address gender inequality, it has the potential to provide the tools women need to both identify and report experiences of violence (Okenwa-Emgwa and Strauss 2018). Similarly, women’s legal status is especially important because it provides them with access to institutions and legal frameworks they can resort to when experiencing violence (Tavares and Quentin 2018). Not only does “legal status” include explicit regulation against violence experienced by intimate partners, but also legal frameworks protecting their rights to freedom of movement, property ownership, adolescent fertility, and regulations addressing all forms of discrimination (“Women’s Economic Opportunity 2012” n.d.). However, the literature has raised questions on the effectiveness of both education and legal status in raising the stature of women and preventing violence alone. For example, even though the benefits of education alone are evident (“Girl Rising Girls Education Nonprofit” n.d.), in order to effectively reduce violence against women, it is necessary to formulate curricula that include gender equality and information on violence against women (Okenwa-Emgwa and Strauss 2018). In the case of women’s legal status, adequate implementation and observation of such laws are needed for them to be effectively enforced (Tavares and Quentin 2018). Our research interacts with such limitations by exploring whether WEOI’s indicators on education and training and women’s legal status can predict a decrease in positive attitude towards violence.

Methodology

This study consists of two parts. The first part is an exploration of three indicators that the Organisation for Economic Co-operation and Development (OECD) has linked to Violence Against Women (“Inequality - Violence Against Women - OECD Data” n.d.).

The second part of this research uses the Women’s Economic Opportunity Index (WEOI) to predict violence against women. We dissect the index into four out of its five main variables (labour participation, access to finance, women’s legal status, and women’s education and training), and we use these four factors as our independent variables. Our dependent variable is attitudes towards violence against women. We draw a causal graph to predict the factors involved in causing negative attitudes towards violence, and then we then run several univariate and multivariate regression models were between our dependent and independent variables.

We conclude that from our four main indicators, Women’s Legal Status seems to have the biggest potential to predict a decrease in attitude towards violence. From there, we highlight subvariables within women’s legal status that have a relationship with our main dependent variable. Finally, we analyze the causal relationship between the democracy index and the legal status of women, which may be influencing attitudes towards women

Theory/Hypotheses

With the literature in mind, and with a curiosity towards measurements taken by the Women’s Economic Opportunity Index, our research question consists on how do labour participation, women’s legal status, access to finance and women’s education and training appear to reduce or increase violence against women? Specifically, which one of those four variables seem to be the best predictor for an increase or reduction in attitude towards violence?

Even though we explore four out of the five indicators used in WEOI in order to possibly find out which indicators seem to best predict attitude towards violence, we also pay attention to the interactions between such indicators. We do so because we are well aware of possible confounding variables.

Even though we highlight Women’s Legal Status as a significant predictor to Attitude Towards Violence, We argue that it’s not possible to isolate one single indicator that seems to impact attitude towards women the most.

An Overview of the Data

Our dependent variables consists on the three indicators by the OECD. The first indicator is Attitude Towards Violence, which represents the share of women who agree that a husband is justified in beating his wife. The second is Prevalence of Violence in Lifetime (0-100%), which indicates the share of women exposed to physical and/or sexual violence from an intimate partner at least once in their lives. Finally, Laws on Domestic Violence (0.25-1) measures whether legal frameworks adequately protect women from domestic violence. It is measured on a scale from 0 to 1, where 1 indicates that the laws are completely discriminatory against women.

Our independent variables are the main indicators in the Women’s Economic Opportunity Index (WEOI). Created by the Economist Intelligence Unit, the research division of the Economist Group, this index “is a dynamic quantitative and qualitative scoring model, constructed from 29 indicators, that measures specific attributes of the environment for women employees and entrepreneurs in 128 economies” (“Women’s Economic Opportunity 2012” n.d.). The index is measured on a scale from 0 - 100 where 0 refers to the least favorable situation for women and 100 refers to the most favorable situation to women.

We have decided to drop the fifth main indicator “General Business Environment.” Whereas this could be a good indicator for observing practical obstacles for the women in the workplace, we thought that focusing on the first four indicators could make our research more consistent.

Labour participation consists in four indicators: equal pay for equal work, non-discrimination, degree of de facto discrimination against women in the workplace, and availability, affordability, and, quality of childcare services (“Women’s Economic Opportunity 2012” n.d.)

Access to finance consists in four indicators: building credit histories, women’s access to finance programmes, delivering financial services, private-sector credit as a percent of Gross Domestic Product (“Women’s Economic Opportunity 2012” n.d.).

Education and training consists of four indicators: School life expectancy (primary and secondary), school life expectancy (tertiary), adult literacy rate, and existence of government or non-government programmes offering small and medium-sized enterprise (SME) support/development training (“Women’s Economic Opportunity 2012” n.d.).

Women’s legal status consists of five indicators: existence of laws protecting women against violence, freedom of movement, property ownership rights, adolescent fertility rate, and whether a country ratification of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) (“Women’s Economic Opportunity 2012” n.d.).

Data Analysis

Part 1: Exploration of Dependent and Independent Variables

Visualizing the Dependent Variables: The OECD Violence Against Women Indicators

In our first stage of data exploration, we determine whether there is an association between a country’s economy and its citizens’ attitudes towards violence against women. Intuitively, we expected wealthier and more developed economies to have more economic opportunities for women, leading to more favourable attitudes towards women.

There seems to be a clear association between attitude towards violence against women and a country’s income group. High and upper middle income countries seem to have a lower share of women who agree that the husband is justified in inflicting violence on their wives.

Similarly to attitudes towards violence, when it comes to prevalence of violence in the lifetime, we can observe that the share of women who have experienced violence in high-income countries (colored red) is concentrated towards the left side of the graph. This means that fewer women in these countries have been exposed to physical or sexual violence in comparison to countries falling in other income groups.

Compared with the other indicators, laws on domestic violence does not appear to have a clear association with income group. In the graph, we can see that most countries, regardless of income group, fall in the “0.75” measure for Laws on Domestic Violence, suggesting that their laws and practices are insufficcient to guarantee the well-being of women. Even though there are very few countries with a value of 1 unit (laws and practices are completely discriminatory against women), it is incredibly worrying that most countries in the globe still have insufficcient laws to protect women’s rights. We also observe that there is a higher proportion of countries with favourable laws towards women (indicator value at level 0.25) among high income and upper middle income countries. For both lower middle income and low income, however, there seems to be more countries in the 0.5 and 0.75 levels than in the 0.25 level.

This unveils a pattern where higher income countries, despite having a large proportion of countries with an indicator level equal to 0.75, also have a more countries with somewhat beneficial laws and practices against domestic violence. However, this does not seem to be a causal effect. It is highly unlikely that a country’s income group alone establishes the laws on domestic violence, but it points us in the direction of establishing a connection between higher levels of economic development and better legislation on domestic violence.

We proceed to plot stacked bars that can help us better understand patterns in the data, specifically when it comes to laws on domestic violence. However, one important aspect to note is that the total number of countries in each income category will affect the visualization of these stack bars when they are not designed to take into account proportion:

# A tibble: 4 × 2
  IncomeGroup         count
  <fct>               <int>
1 High income            39
2 Upper middle income    33
3 Lower middle income    37
4 Low income             19

There are 39 high income, 33 upper middle income, 37 lower middle income, and only 19 lower income countries in the data set. This is important to keep in mind when looking at barstacks that only include count instead of frequency (Attitude Towards Violence by Income Group).

A stacked bar helps in the visualization and reveals that indeed most of the countries that have a value of 0.25 on Laws on Domestic Violence (better laws on domestic violence) are high income countries. However, it is important to note that in the 0.75 value, there seems to be a more balanced distribution throughout all income groups. In the value of “1,” i.e., laws on domestic violence that completely discriminate against women, we find only upper middle income countries.

We also produced a bar stack for prevalence of violence in the lifetime, but instead of using a proportion stacked bar we opted for a frequency one. Even though this plot suffers from the total number of high income countries being greater than the number of countries in other income categories, the concentration of such countries in the left side of the graph once again reveal the pattern of high income countries performing better in such indicators.

After drawing analysis by Income Group, we make a comparison between all three indicators to determine their relationship to one another.

Intuitively, we would expect prevalence of violence and attitudes towards violence to have a positive linear relationship: As more women agree that their husbands are justified in beating them, domestic violence will become more widespread. We create a scatter plot to verify that our data follows this intuition.

The x-axis shows attitudes towards violence, the y-axis shows prevalence of violence in the lifetime, and the color indicates the level of laws on domestic violence. The visualization shows a positive linear relationship between prevalence of violence in the Lifetime and attitudes towards violence (correlation coefficient = 0.45).

Looking at laws on domestic violence, we see the the yellow countries (level equals 0.25) are concentrated in the bottom left side of the plot, thus revealing that countries that have better laws on Domestic Violence also tend to a lower prevalence of domestic violence and better attitudes towards women. However, the 0.75, 0.5 and 1 levels of laws do not appear to have a particular pattern and are spread throughout the graph.

The annotated countries in the graph are the two countries where the laws on domestic violence are completely discriminatory against women: Equatorial Guinea and the Russian Federation. Equatorial Guinea has a higher prevalance of violence and worse attitudes towards women, which agrees with our intuition. However, the Russian Federation is an interesting case because it has a relatively low prevalence of violence and better attitudes towards violence, but its legislation completely discriminates against women. It is closer to the yellow dots on the graph, which are countries with better laws on domestic violence. This sets Russia apart as a notable outlier when plotting the relationship between the three dependent variable. It would be interesting to conduct further research on Russia when analyzing potential causes for the persistence of violence against women.

Comparing OECD and UN in Prevalence of Violence Against Women

To determine whether the OECD indicator is an accurate representation of the prevalence of domestic violence against women, we decided to compare it to other available indicators on domestic violence. We have chosen the UN indicator, “Proportion of women subjected to physical and/or sexual violence by a current or former intimate partner in the last 12 months” (“Gender Statistics - Violence Against Women” n.d.).

There are some clear distinctions between the UN and OECD indicators. While they both measure the proportion of women who have experienced violence by an intimate partner, the UN indicator only includes women who have experienced violence in the past 12 months, while the OECD includes those who experienced violence at any point in their lives Therefore, we would expect the OECD indicator to be larger than the UN indicator for most countries, given that many more women would have experience violence at some point in their lives.

Another limitation is that the UN indicator comes from many different reference years. Some countries have collected this data in more recent years (2015), while others last collected this data in the year 2000. This discrepancy makes comparison between countries and between indicators more difficult, since peoples’ attitudes towards domestic violence and countries’ laws and regulations may have changed significantly over the years, potentially reducing the prevalence of domestic violence. In addition, all the data from the OECD indicator was collected in 2019, much more recently than most of the UN data. Nonetheless, it would be interesting to find out whether both indicators are consistent with each other.

The figure above shows two maps of the world, with colored markers to represent the prevalence of violence in different countries. The first map visualizes the OECD indicator, while the second map visualizes the UN indicator. As expected, we can see that the OECD map has much darker and larger markers in general than the UN map, suggesting that the prevalence of violence is higher in the OECD data. This makes sense since the OECD measured the prevalence of violence over womens’ lifetimes, while the UN only measured violence in the past 12 months.

The two indicators also appear to be consistent across different regions. For instance, in both maps, countries in the Global North appear to have less prevalence of domestic violence, as shown by the smaller, lighter markers. In contrast, markers across the Global South are consistently darker and larger, revealing patterns of higher prevalence of domestic violence.

We use a scatter plot to verify the relation we observed between the UN and OECD indicators. As we can see, the indicators appear to have a very strong positive relationship, with a correlation coefficient of 0.7561751. This comparison verifies the accuracy of the OECD data by comparing it to an external source.

Mapping Attitudes Towards Violence

Having observed the patterns of prevalence of violence across world regions, we now look at attitudes towards violence from the same perspective.

This map shows the attitudes towards violence against women across different countries. Similarly to the pattern we observed in prevalence of violence, the wealthier and more developed countries of the global North seem to have a lower share of women who justify violence than countries in the global South. This observation further reinforces our interest in explaining these indicators using measures of economic development and opportunity.

Issues with the Dependent Variable Data and Limitations

Before moving on with our analysis, it is important to note the limitations and issues with our dependent variables.

Categorizing countries by income group might reveal different patterns of violence against women across countries of different income levels, but it tells us little about the causes of these discrepancies. Hence, whereas our graphs were helpful in revealing possible paths we can take in our research, they did not necessarily reveal the causes of violence against women. In addition, using income group as a category might ignore nuances regaridng the socio-economic development of different countries. For example, Brazil is classified as an Upper Middle Income country, but it is also one of the most unequal countries in the world (Gini Coefficient = 53.3), ranking 9th globally. Such great inequality could be driving some of our dependent variables, and this would not be revealed by income group.

The OECD indicators themselves also have several limitations. There are some discrepancies between what the indicators are trying to measure and what they actually measure. Since violence comes in many diverse forms, it is difficult to perfectly define it or accurately measure it. For example, the prevalence in lifetime indicator only quantifies violence from an intimate partner, excluding levels of harassment that come from outside intimate circles, such as rape. Similarly, to measure “attitude towards violence,” the data set creators used “the percentage of women who agree that a husband/partner is justified in beating his wife/partner under certain circumstances.” Why is this particular question used as a proxy for attitude towards violence? Why are the men not asked whether they are justified in beating women? Are there better or more comprehensive questions to gauge attitude towards violence?

In addition, for the “laws on domestic violence” indicator, the dataset creators do not explain how they quantified the abstract concept of “laws and practices.” They also do not specify what they mean by laws that “fully discriminate against women’s rights.” This makes it difficult to determine the accuracy of the indicator.

The dataset also does not contain all the countries in the world. If we perform an anti-join of the countries dataset with the violence dataset, we can see that there are around 82 missing countries. Many of these countries are small islands that may not have enough data on violence against women.

Visualizing the Independent Variables

A Note on our Independent Variables Dataset

Our independent variables come from the Women’s Economic Opportunities Index, a composite index created by the Economist Intelligence Unit that evaluates whether a country’s environment is favorable towards female entrepreneurs and employees (“Women’s Economic Opportunity 2012” n.d.). The index was measured in two years: first in 2010, and later in 2012. We use the most recent 2012 dataset in our analysis. We downloaded the full dataset of this index as an Excel workbook from the Economist Intelligence Unit’s website. This original version can also be found in this repository under data/violence_factors. However, this Excel workbook contains several sheets and is not readable by dplyr’s csv importer, so we had to manually select the sheet we needed from Excel, clean it, and export it in a .csv format to import it into R Studio. In particular, we used the Excel sheet named Data2012, which contains the 2012 index score for every country, in addition to the breakdown of each score into its categories and sub-categories. Since the sheet was not in csv format and contained several merged columns and rows, we had to clean it up manually in Excel before importing it as a csv file into R Studio. The cleaned version of the sheet can be found in the file data/violence_factors/woe_data_cleaned.csv.

Here, we provide a brief exploration of our independent variables. We plot a histogram for the WEO Index as well as each of the four categories that fall under the index.

As the histograms show, all the categories as well as the indicator lie on a scale from 0 to 100, where 0 is least favourable for women and 100 is more favourable. We see that the education and training indicator and the legal status indicator are skewered more to the left of the scale, suggesting that most countries have favorable education and legal status for women. However, in the access to finance indicator, countries appear to be spread more uniformly across the scale. In terms of labour policy, most countries lie in the 50 units range. Finally, the WEO index itself appears to have a somewhat bimodal distribution with a peak around the 50 units range and another at the 75 units range.

Limitations of the Independent Variables

Our independent variables also have some limitations that may make it difficult to compare them with our dependent variables. Firstly, one important limitation is the difference in dates between our dependent and independent variables. Whereas the OECD violence against women data was measured in 2019, the WEOI was measured back in 2012. It might be the case that countries have significantly improved or worsen their scores in the WEOI in the time between 2012 and 2019, which could bias our analysis.

In addition, one major limiting factor is the fact that the variables we have explored in the Women’s Economic Index Composite index are composite variables. Hence, if we are looking for one specific variable that could explain violence against women, it would be unclear from the composite indicator which particular underlying indicator is the causal one. The large number of indicators (29) that contribute to the WEO index may give rise to many confounding variables that complicate our analysis. If all the independent indicators are correlated, there might be a confounding variable causing all of them and causing our dependent variables at the same time.

Similarly to our dependent variables, the WEO index cannot accurately or perfectly measure an abstract, complicated concept such as women’s economic opportunities. A lot of human biases and motivations are involved in the process of selecting which variables to combine into a single indicator. For example, since the indicator was developed by The Economist and sponsored by Exxon-Mobil, they mostly considered private sector entrepreneurship and initiatives as a measure of women’s economic opportunities, rather than taking into account public policy and state-sponsored opportunities. In addition, many of the qualitative indicators in the index, such as the availability and affordability of childcare, did not exist and had to be created and measured by analysts from the Economist Intelligence Unit (“Women’s Economic Opportunity 2012” n.d.). Therefore, these qualitative assessments were made by external agents rather than being made by national statistics agencies, possibly introducing external biases into the data and excluding local knowledge and data.

A final limitation is that our independent variable was only measured for 128 countries, providing us with less data to build our regression model on

Part 2: Predicting Attitude Towards Violence

Correlation Coefficients

Having explored out dependent and independent variables separately, we proceed to explore the relationship between them.

First, we want to get a general sense of the relationship between the women’s economic opportunity index and the three dependent variables. Is the WEOI correlated at all with our variables?

           [,1]
attitude  -0.68
law       -0.17
prev_viol -0.37

We can infer from the correlation coefficients that the WEOI is negatively correlated with all of the dependent variables to varying extents, as we would have expected. As women receive more economic opportunities, we would expect the prevalence of domestic violence to decrease, attitudes towards women to become more favourable, and laws on domestic violence to become more supportive of survivors.

We are intrigued by the fact that attitudes towards violence against women has the strongest correlation with the WEOI (correlation coefficient = -0.68). Therefore, we decided to proceed with this variable and further examine its relation with the categories that make up the WEOI.

We can see from this correlation matrix that attitudes towards violence against women has a moderate to strong negative correlation with all the WEOI categories, especially education and training. In addition, the WEOI indicators seem to be strongly correlated positively with each other. Intuitively, it makes sense for these correlations to exist as countries where women are better educated are usually those where women have better access to finance, a better legal status, and more favourable labour policies. However, these correlations complicate our effort to find an explanatory variable for violence against women, as it is difficult to disentangle which of these indicators is responsible for any effects we observe on attitudes towards violence.

Causal Diagram

To help us better understand the complicated relationships between the different variables, we draw a causal diagram.

This diagram showcases the relations between both the dependent and independent variables, incorporating external variables as well. We assumed that, among our dependent variables, prevalence of violence against women can be caused by discriminatory laws on domestic violence and supportive attitudes towards violence. Among our independent variables, we noted that the four categories (labour policy and practice, education and training, woman’s legal status and access to finance) are all direct causes of the WEOI, since the index is composed of them. In addition, one common cause we noted for all of these variables could be democracy, since democratic countries are likely to have political structures that are conducive to the economic empowerment of women.

We now use our causal diagram to determine the set of variables we need to adjust for when predicting our dependent variables using our independent variables.

We only run the adjustment set method on one independent variable: legal status. However, the resulting adjustment sets will be analogous for the other independent variables, since they all share the same relationships. We find that there are two sets of variables we may need to adjust for: the level of democracy in a country, or the rest of the independent variables (finance, education, labour policy). We may need to adjust for democracy because it is a direct cause of all our independent variables. We may also need to adjust for the other independent variables since they can all potentially be causes of attitudes towards violence.

Regressions

Next, we run regression models to evaluate whether there is a linear relationship between attitudes towards violence and our independent variables. We first run univariate regressions between the attitudes indicator and each of the independent indicators to observe their individual effects before making adjustments.

                   Estimate  Est.Error       Q2.5      Q97.5
Intercept        45.9798093 5.44521076 35.3148566 56.6867804
womens_opp_index -0.3365496 0.08828696 -0.5078238 -0.1634523

We find that with evey one unit increase in the women’s oppurtunity index, there is a decrease of -0.34% in the share of women who agree that men are justified in beating their wives. This effect has a very low standard error and a narrow confidence interval, which means there is less uncertainty about this relationship.

Next, we model the relationship between attitudes towards violence and each of the four categories.

With every one unit increase in Labour Policy and Practice there is a decrease of -0.5 in attitudes towards violence. This variable also has a low standard error of NA, meaning we can be fairly certain that labour policy has a negative impact on the share of women who justify being abused. The 95% confidence interval for the coefficient ranges from NA to NA.

                Estimate  Est.Error       Q2.5      Q97.5
Intercept      44.019126 3.55073470 37.0032868 50.9391982
finance_access -0.494154 0.06649971 -0.6239228 -0.3670876

The estimate for the effect of Access to Finance is on a similar scale as the previous two variables. With every one unit increase in Access to Finance, there is a decrease of -0.49 in attitudes towards violence. The uncertainty level is a little less than labour policy and the 95% confidence intervals are also smaller.

Similarly, with every one unit increase in Education and Training there is a decrease of -0.61 in attitudes towards violence. This is a more significant change than the previous two variables explored, with a similarly small standard error and narrow confidence interval. Therefore, we can be fairly certain that education has a more significatn effect on attitudes than the previous two variables.

               Estimate Est.Error      Q2.5      Q97.5
Intercept    80.4053030 6.7590671 66.918922 93.7008655
legal_status -0.8357202 0.0924215 -1.017401 -0.6541103

The estimate for Women’s Legal Status is -0.84. This is the most significant effect we found among our independent variables, despite all our regressions having similarly low levels of uncertainty.

With these individual effects in mind, we run a multivariate regression to adjust for the effects of all the independent variables on the dependent variable:

                  Estimate Est.Error        Q2.5      Q97.5
Intercept      69.70911748 6.6809380 56.42001127 83.0106261
labour_policy   0.20846045 0.1147448 -0.02086275  0.4371212
education      -0.42351936 0.1299135 -0.67502192 -0.1678487
legal_status   -0.44786423 0.1581261 -0.76305987 -0.1371629
finance_access -0.04195654 0.0973169 -0.23592304  0.1518785

After running this multivariate regression, we see that for many of our factors, the estimated coefficient decreased significantly while the uncertainty levels increased. Labour policy now has a positive estimated effect rather than a negative one, but its confidence interval spans both negative and positive values, which indicates that it could have no effect on attitudes towards violence. While the other variables still have negative effects on attitudes towards violence, access to finance has a negligible estimated effect and its interval is very wide, spanning 0. The two variables that seem to be influencing attitudes most significantly are education and legal status, with very similar estimated coefficients, estimated errors, and confidence intervals.

It is unclear what the results of this regression mean in terms of the interdependence of the independent variables. Could legal status and education be confounding variables that are causing the effects we saw in labour policy and access to finance? This regression is insufficient to draw such conclusions.

Legal status and education appear to have the strongest effects on attitudes towards violence, but they are both composite measurements made of several indicators. Therefore, we select one of them (legal status) and break it down into its components to pinpoint exactly which one is driving attitudes towards violence.

Breakdown of Legal Status

                       Estimate   Est.Error        Q2.5       Q97.5
Intercept           77.54754765 10.74903872 56.79215736 98.98115039
addressing_violence -0.06114097  0.07452694 -0.20832209  0.08438125
citizen_rights      -0.12425004  0.13374480 -0.37838164  0.14486701
prop_ownership      -0.19342060  0.05841844 -0.30641243 -0.07795445
adol_fertility      -0.15884840  0.06874776 -0.29353805 -0.02343940
contraceptive_use   -0.18067836  0.06783510 -0.31216844 -0.04898670
cedaw               -0.08810608  0.06020421 -0.20649332  0.02767044
political_part       0.08406504  0.07999188 -0.07801288  0.23800850

When running a regression on further exploration of the indicator legal status, we found that within the factors under legal status the one with the highest effect on attitudes towards violence was property ownership with a negative decrease of -0.19% in attitudes towards violence for every unit increase in property ownership. This factor also had the lowest uncertainty level and the most narrow confidence interval among the legal status indicators. However, its effect on attitudes is still very low (the upper end of the confidence interval is almost 0), as are the rest of the variables.

Upon closer inspection, it appears that property ownership rights is a discrete variable with five categories. While attitudes towards violence appear to be concentrated at the bottom for countries with the highest levels of property ownership rights for women (level 100), this association is not very defined or clear for other categories. This explains why the effect estimated by the regression is so low.

Intuitively, it makes some sense for property ownership rights to have an impact on attitudes towards violence. Having property ownership rights makes women less dependent on their spouses to secure a home and make a living, which means they can afford to leave an abusive relationship. However, in countries where women do not have many property ownership rights (e.g. they cannot inherit property from their parents or relatives), women have no other resort than to remain in an abusive relationship, so they tend to justify their husbands’ abuse as a mechanism to deal with their situation (e.g. “He only hit me because I offended him; if I behave he will not hurt me, etc.”). Therefore, property ownership rights may have an effect on attitudes towards violence, but given the limitations in our data, it is unclear how significant these effects may be. In addition, we would expect other variables in this indicator, such as use of contraception and political participation, to have a stronger effect on attitudes towards violence.

Exploring the Effects of Democracy

Next, we adjust our regression for the effects of democracy. As a proxy for democracy, we use the Democracy Index measured annually by the Economist Intelligence Unit. The index is measured on a scale of 1 to 10, where 1 is least democratic and 10 is most democratic. We use the most recent measurements made in 2020, and we run three regressions. First, we run a regression that predicts legal status using the democracy index to ensure that, as our causal diagram expected, democracy is a cause of improved legal status for women. Second, we look at the relationship between attitudes and democracy to ascertain whether democracy also drives attitudes towards violence. Finally, we run a regression that explains attitudes towards violence using both democracy and legal status. If democracy truly is a causal variable that explains legal status, we would expect to see a significant decrease in the effect of democracy on attitudes in the multivariate regression, since all of its effects will be transferred to legal status.

                 Estimate Est.Error      Q2.5     Q97.5
Intercept       34.022438  3.511779 27.140653 41.045836
democracy_index  6.065459  0.531007  5.009212  7.101252

                 Estimate Est.Error      Q2.5    Q97.5
Intercept       66.695393 4.5393282 57.951482 75.85373
democracy_index -6.995572 0.7486859 -8.468195 -5.55805

                  Estimate Est.Error       Q2.5      Q97.5
Intercept       78.9938339 6.7951249 65.8375558 92.4179898
democracy_index -1.5105625 1.1184484 -3.7447150  0.7071513
legal_status    -0.6850411 0.1440593 -0.9726755 -0.3996468

The first regression reaffirms our suspicions that the democracy index does drive the legal status. There appears to be a strong, positive correlation between the two variables, where for every unit increase in the democracy index, there is a 6.07 unit increase in women’s legal status. This large coefficient is likely a result of the difference between the scales of the two variables.

The second regression also shows that the democracy index drives attitudes towards violence. The negative relationship indicates that as democracy increases, attitudes towards violence tend to decrease. It is interesting to note that the coefficients for both regressions are very close, but it is not clear how this should be interpreted.

The multivariate regression of legal status and democracy on attitudes towards violence confirms our suspicion that democracy is a cause of legal status. The effect of the democracy index on attitudes towards violence dropped to -1.51, with a relatively large standard error that makes the confidence interval wide and spanning 0. In contrast, the effect of legal status on attitudes also decreased, but the standard remained low and the confidence intervals are narrow. This suggests that there is still a significant relation between the legal status of women and attitudes towards violence. In addition, this result could support our assumption that democracy is a cause of legal status, since we can see that the effects of democracy on attitudes are being masked by the legal status of women, which lies between the variables.

Analysis and Interpretation of Results

When we ran the regression between attitudes towards violence individually with each of the four indicators from the WEOI, we noticed that women’s legal status and education seemed to be the most strongly negatively correlated with the dependent variable. Running a multivariate regression with all indicators, however, decreased the correlation of both variables and increased their uncertainty levels. Even though they both remain moderately correlated, it is difficult to isolate a single cause for the increase or decrease of levels in attitudes towards violence. Interestingly, when we ran the multivariate regression, the other two variables (labour policy and access to finance) had negligible effects on attitudes towards violence, revealing a deeper interplay between the variables that was difficult to clarify.

A counterfactual that arises from our research is a country in which women’s legal status is significantly lower than the rest of the indicators. Would such a country still have worse attitudes towards violence against women (more women agreeing that violence is justified)? Even though we cannot find such a case within the indicators of the WEOI, the exploration on the dependent variables highlighted the Russian Federation as having laws on domestic violence that completely discriminated against women, but relatively women justified domestic violence. While we do not have data on Russia’s legal status as part of the WEOI, further study on the case of the Russian Federation could reveal the effectiveness of women’s legal status in causing a decrease in violence against women.

After further examining women’s legal status, we found that most of the indicators within this category seem to have negligible effects on attitudes towards violence, and the indicator with the strongest effect was property ownership rights. However, even this factor had a very small effect on attitudes towards violence, and we would have expected other factors to be more impactful.

After analyzing the causal relationship between the democracy index and the legal status of women, we note that democracy seems to be driving women’s legal status up, which in turn causes attitude towards violence to decrease. This was an interesting finding: the higher the levels of democracy, the better the legal status of women, which in turn produces lower levels of acceptance of violence against women. We can hypothesize that there may be a similar causal pattern between legal status and the other categories (education, access to finance, and labour policy), since democracy can be a driver of all of them.

Hence, our findings do corroborate to some extent the hypothesis that strengthening democracy and, hence, the legal status of women, might be a path for improving attitudes towards violence against women, and thereby decreasing violence against women. However, given our use of composite indicators such as the democracy index and the WEOI, it is unclear exactly how democracy should be strengthened, or what aspect of women’s legal status is the most significant in driving attitudes towards violence. In addition, since we could not test the counter-factual hypothesis or all the possible confounding variables, we are uncertain if this relationship is purely causal, or if it is influenced by an unseen external variable.

Of course, it is important to keep in mind the limitations of our data mentioned earlier. The democracy index and the WEOI are both composite indicators created by the Economist Intelligence Unit, an institution that may be imposing its own biases and motivations on these indices. Similarly, the OECD violence against women indicators do not exactly measure violence against women, but rather proxies that were chosen by analysts and experts. The relationships we see between these proxies may not necessarily persist when applied to the diverse definitions and understandings of these concepts around the world.

Conclusions

We cannot single out or highlight one single indicator in the WEOI that is most influential in decreasing violence against women, but we can see that education and legal status have reasonable effects on attitudes towards violence. Our research highlights the complexities and the varying number of factors that result in the persistence of violence against women. However, it still might be useful to investigate further the sub-indicators within each indicator for the WEOI and analyze what they can provide as insights. Singling out outlier countries and counterfactuals, like Russia, and closely analyzing violence against women through both quantitative and qualitative research can further reveal indicators that can be useful in measuring degrees of such forms of gendered violence.

While the four indicators we observed and analysed did in fact correlate with better attitudes towards violence, there are confounding variables that we perhaps have not considered, making it difficult to come to a causal conclusion. We consider one causal variable, democracy, that may be driving these effects, and discover that it could potentially be an explanatory variable for legal status.

We found the “top-to-bottom” structure of this research, in which we started from one index and peeled off its layers was extremely insightful in terms of shedding light into how complex indexes and composite variables are constructed. It reminds us that the construction of such indexes are attempts to measure incredibly abstract concepts into truly useful ones. Whereas this was helpful for us in schematizing causal diagrams, we also found ourselves constantly puzzled by what we were truly measuring.

References